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Optimal Control of Dynamic Systems Driven by Vector Measures: Theory and Applications

11,017 ₹14,304 22% OFF
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Updated 24th Sep 2026, 01:06 AM
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Lowest Ever
₹11,017
24th Sep 2026
Highest Ever
₹11,017
24th Sep 2026
Average Price
₹11,017
2 data points
3-Month Low
₹11,017
24th Sep 2026
6-Month Low
₹11,017
24th Sep 2026
12-Month Low
₹11,017
24th Sep 2026

Price History

As per price history, Optimal Control of Dynamic Systems Driven by Vector Measures: Theory and Applications's lowest price is ₹11,017. The average price is ₹11,017. Highest ever price is ₹11,017. Along with deals, coupons & discounts, you can get it at ₹11,217. Read our Pricing Disclosure

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Product Details

Product Code 3030821412
Market Place amazon.in
Store Amazon
Brand Springer
Category Rugs
Manufacturer Springer Nature
Color Sd-mo30
Country of Origin India

Frequently Asked Questions

What is the lowest price ever for Optimal Control of Dynamic Systems Driven by Vector Measures Theory and Applications?
The all-time lowest price for Optimal Control of Dynamic Systems Driven by Vector Measures Theory and Applications was ₹11,017 recorded on 24th Sep 2026. The current price matches or is below the all-time low!
What was the highest price for this product?
The highest recorded price for Optimal Control of Dynamic Systems Driven by Vector Measures Theory and Applications was ₹11,017 on 24th Sep 2026.
What is the average selling price?
Based on 2 data points, the average price of Optimal Control of Dynamic Systems Driven by Vector Measures Theory and Applications is ₹11,017. The current price is 0% above average. Consider waiting for a price drop.
What is the 3-month lowest price?
In the last 3 months, the lowest price was ₹11,017 on 24th Sep 2026.
What is the 6-month lowest price?
The 6-month lowest price for Optimal Control of Dynamic Systems Driven by Vector Measures Theory and Applications was ₹11,017 on 24th Sep 2026.
Should I buy this product now?
Price is high. Score: 0/100 (Grade F) — we recommend waiting. The product has been available at significantly lower prices in the past.
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